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STATISTICS (ST)

Dr. Mike Conerly, Department Chair
Office: 300 Alston Hall

Students interested in majoring in statistics should follow Option II of the Program in Operations Management.

In addition to completing the specific prerequisites included in the descriptions of the following courses, all students seeking to enroll in 300- or 400-level courses in the Culverhouse College of Commerce and Business Administration must have attained junior standing (61 semester hours).

All C&BA students must, prior to seeking to enroll in any 300- or 400-level C&BA course, complete or be enrolled in the following prerequisites: EC 110 and EC 111; MATH 112 and MATH 121, or MATH 115 and MATH 125; CS 102; AC 210; ST 260; and LGS 200 (or their equivalents); and at least 4 hours in natural science, 3 hours of fine arts, literature, or humanities, and 3 hours of history or social and behavioral sciences. Failure to fulfill all prerequisites prior to enrolling in a 300- or 400-level C&BA course will result in administrative disenrollment from that course.

Development of fundamental concepts of organizing, exploring, and summarizing data; probability; common probability distributions; sampling and sampling distributions; estimation and hypothesis testing for means, proportions, and variances using parametric and nonparametric procedures; power analysis; goodness of fit; contingency tables. Statistical software packages are used extensively to facilitate valid analysis and interpretation of results. Emphasis is on methods and on selecting proper statistical techniques for analyzing real situations.

Analysis of variance and design of experiments, including randomization, replication, and blocking; multiple comparisons; correlation; simple and multiple regression techniques, including variable selection, detection of outliers, and model diagnostics. Statistical software packages are used extensively to facilitate valid analysis and interpretation of results. Emphasis is on appropriate analysis of data in real situations.

Modeling issues for multiple linear regression are discussed in the context of data analysis. These include the use of residual plots, transformations, hypothesis tests, outlier diagnostics, analysis of covariance, variable selection techniques, weighted least squares, and collinearity. The uses of logistic regression are similarly discussed for dealing with binary-valued independent variables.